Matching Items (6)
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- All Subjects: Cloud Computing
- All Subjects: Wireless communication systems
- Creators: Sen, Arunabha
Description
The pay-as-you-go economic model of cloud computing increases the visibility, traceability, and verifiability of software costs. Application developers must understand how their software uses resources when running in the cloud in order to stay within budgeted costs and/or produce expected profits. Cloud computing's unique economic model also leads naturally to an earn-as-you-go profit model for many cloud based applications. These applications can benefit from low level analyses for cost optimization and verification. Testing cloud applications to ensure they meet monetary cost objectives has not been well explored in the current literature. When considering revenues and costs for cloud applications, the resource economic model can be scaled down to the transaction level in order to associate source code with costs incurred while running in the cloud. Both static and dynamic analysis techniques can be developed and applied to understand how and where cloud applications incur costs. Such analyses can help optimize (i.e. minimize) costs and verify that they stay within expected tolerances. An adaptation of Worst Case Execution Time (WCET) analysis is presented here to statically determine worst case monetary costs of cloud applications. This analysis is used to produce an algorithm for determining control flow paths within an application that can exceed a given cost threshold. The corresponding results are used to identify path sections that contribute most to cost excess. A hybrid approach for determining cost excesses is also presented that is comprised mostly of dynamic measurements but that also incorporates calculations that are based on the static analysis approach. This approach uses operational profiles to increase the precision and usefulness of the calculations.
ContributorsBuell, Kevin, Ph.D (Author) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Lindquist, Timothy (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Contention based IEEE 802.11MAC uses the binary exponential backoff algorithm (BEB) for the contention resolution. The protocol suffers poor performance in the heavily loaded networks and MANETs, high collision rate and packet drops, probabilistic delay guarantees, and unfairness. Many backoff strategies were proposed to improve the performance of IEEE 802.11 but all ignore the network topology and demand. Persistence is defined as the fraction of time a node is allowed to transmit, when this allowance should take into account topology and load, it is topology and load aware persistence (TLA). We develop a relation between contention window size and the TLA-persistence. We implement a new backoff strategy where the TLA-persistence is defined as the lexicographic max-min channel allocation. We use a centralized algorithm to calculate each node's TLApersistence and then convert it into a contention window size. The new backoff strategy is evaluated in simulation, comparing with that of the IEEE 802.11 using BEB. In most of the static scenarios like exposed terminal, flow in the middle, star topology, and heavy loaded multi-hop networks and in MANETs, through the simulation study, we show that the new backoff strategy achieves higher overall average throughput as compared to that of the IEEE 802.11 using BEB.
ContributorsBhyravajosyula, Sai Vishnu Kiran (Author) / Syrotiuk, Violet R. (Thesis advisor) / Sen, Arunabha (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2013
Description
Resource allocation is one of the most challenging issues policy decision makers must address. The objective of this thesis is to explore the resource allocation from an economical perspective, i.e., how to purchase resources in order to satisfy customers' requests. In this thesis, we attend to answer the question: when and how to buy resources to fulfill customers' demands with minimum costs?
The first topic studied in this thesis is resource allocation in cloud networks. Cloud computing heralded an era where resources (such as computation and storage) can be scaled up and down elastically and on demand. This flexibility is attractive for its cost effectiveness: the cloud resource price depends on the actual utilization over time. This thesis studies two critical problems in cloud networks, focusing on the economical aspects of the resource allocation in the cloud/virtual networks, and proposes six algorithms to address the resource allocation problems for different discount models. The first problem attends a scenario where the virtual network provider offers different contracts to the service provider. Four algorithms for resource contract migration are proposed under two pricing models: Pay-as-You-Come and Pay-as-You-Go. The second problem explores a scenario where a cloud provider offers k contracts each with a duration and a rate respectively and a customer buys these contracts in order to satisfy its resource demand. This work shows that this problem can be seen as a 2-dimensional generalization of the classic online parking permit problem, and present a k-competitive online algorithm and an optimal online algorithm.
The second topic studied in this thesis is to explore how resource allocation and purchasing strategies work in our daily life. For example, is it worth buying a Yoga pass which costs USD 100 for ten entries, although it will expire at the end of this year? Decisions like these are part of our daily life, yet, not much is known today about good online strategies to buy discount vouchers with expiration dates. This work hence introduces a Discount Voucher Purchase Problem (DVPP). It aims to optimize the strategies for buying discount vouchers, i.e., coupons, vouchers, groupons which are valid only during a certain time period. The DVPP comes in three flavors: (1) Once Expire Lose Everything (OELE): Vouchers lose their entire value after expiration. (2) Once Expire Lose Discount (OELD): Vouchers lose their discount value after expiration. (3) Limited Purchasing Window (LPW): Vouchers have the property of OELE and can only be bought during a certain time window.
This work explores online algorithms with a provable competitive ratio against a clairvoyant offline algorithm, even in the worst case. In particular, this work makes the following contributions: we present a 4-competitive algorithm for OELE, an 8-competitive algorithm for OELD, and a lower bound for LPW. We also present an optimal offline algorithm for OELE and LPW, and show it is a 2-approximation solution for OELD.
The first topic studied in this thesis is resource allocation in cloud networks. Cloud computing heralded an era where resources (such as computation and storage) can be scaled up and down elastically and on demand. This flexibility is attractive for its cost effectiveness: the cloud resource price depends on the actual utilization over time. This thesis studies two critical problems in cloud networks, focusing on the economical aspects of the resource allocation in the cloud/virtual networks, and proposes six algorithms to address the resource allocation problems for different discount models. The first problem attends a scenario where the virtual network provider offers different contracts to the service provider. Four algorithms for resource contract migration are proposed under two pricing models: Pay-as-You-Come and Pay-as-You-Go. The second problem explores a scenario where a cloud provider offers k contracts each with a duration and a rate respectively and a customer buys these contracts in order to satisfy its resource demand. This work shows that this problem can be seen as a 2-dimensional generalization of the classic online parking permit problem, and present a k-competitive online algorithm and an optimal online algorithm.
The second topic studied in this thesis is to explore how resource allocation and purchasing strategies work in our daily life. For example, is it worth buying a Yoga pass which costs USD 100 for ten entries, although it will expire at the end of this year? Decisions like these are part of our daily life, yet, not much is known today about good online strategies to buy discount vouchers with expiration dates. This work hence introduces a Discount Voucher Purchase Problem (DVPP). It aims to optimize the strategies for buying discount vouchers, i.e., coupons, vouchers, groupons which are valid only during a certain time period. The DVPP comes in three flavors: (1) Once Expire Lose Everything (OELE): Vouchers lose their entire value after expiration. (2) Once Expire Lose Discount (OELD): Vouchers lose their discount value after expiration. (3) Limited Purchasing Window (LPW): Vouchers have the property of OELE and can only be bought during a certain time window.
This work explores online algorithms with a provable competitive ratio against a clairvoyant offline algorithm, even in the worst case. In particular, this work makes the following contributions: we present a 4-competitive algorithm for OELE, an 8-competitive algorithm for OELD, and a lower bound for LPW. We also present an optimal offline algorithm for OELE and LPW, and show it is a 2-approximation solution for OELD.
ContributorsHu, Xinhui (Author) / Richa, Andrea (Thesis advisor) / Schmid, Stefan (Committee member) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cloud computing is regarded as one of the most revolutionary technologies in the past decades. It provides scalable, flexible and secure resource provisioning services, which is also the reason why users prefer to migrate their locally processing workloads onto remote clouds. Besides commercial cloud system (i.e., Amazon EC2), ProtoGENI and PlanetLab have further improved the current Internet-based resource provisioning system by allowing end users to construct a virtual networking environment. By archiving the similar goal but with more flexible and efficient performance, I present the design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform, and G-PLaNE that focuses on how to construct the virtual networking environment upon the self-designed resource provisioning system consisting of multiple geo-distributed clusters. Furthermore, I conduct a comprehensive study to layout existing Mobile Cloud Computing (MCC) service models and corresponding representative related work. A new user-centric mobile cloud computing service model is proposed to advance the existing mobile cloud computing research.
After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation.
At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework.
After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation.
At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework.
ContributorsXing, Tianyi (Author) / Huang, Dijiang (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Medhi, Deepankar (Committee member) / Arizona State University (Publisher)
Created2014
Description
Interference constitutes a major challenge for communication networks operating over a shared medium where availability is imperative. This dissertation studies the problem of designing and analyzing efficient medium access protocols which are robust against strong adversarial jamming. More specifically, four medium access (MAC) protocols (i.e., JADE, ANTIJAM, COMAC, and SINRMAC) which aim to achieve high throughput despite jamming activities under a variety of network and adversary models are presented. We also propose a self-stabilizing leader election protocol, SELECT, that can effectively elect a leader in the network with the existence of a strong adversary. Our protocols can not only deal with internal interference without the exact knowledge on the number of participants in the network, but they are also robust to unintentional or intentional external interference, e.g., due to co-existing networks or jammers. We model the external interference by a powerful adaptive and/or reactive adversary which can jam a (1 − ε)-portion of the time steps, where 0 < ε ≤ 1 is an arbitrary constant. We allow the adversary to be adaptive and to have complete knowledge of the entire protocol history. Moreover, in case the adversary is also reactive, it uses carrier sensing to make informed decisions to disrupt communications. Among the proposed protocols, JADE, ANTIJAM and COMAC are able to achieve Θ(1)-competitive throughput with the presence of the strong adversary; while SINRMAC is the first attempt to apply SINR model (i.e., Signal to Interference plus Noise Ratio), in robust medium access protocols design; the derived principles are also useful to build applications on top of the MAC layer, and we present SELECT, which is an exemplary study for leader election, which is one of the most fundamental tasks in distributed computing.
ContributorsZhang, Jin (Author) / Richa, Andréa W. (Thesis advisor) / Scheideler, Christian (Committee member) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2012
Description
Today, many wireless networks are single-channel systems. However, as the interest in wireless services increases, the contention by nodes to occupy the medium is more intense and interference worsens. One direction with the potential to increase system throughput is multi-channel systems. Multi-channel systems have been shown to reduce collisions and increase concurrency thus producing better bandwidth usage. However, the well-known hidden- and exposed-terminal problems inherited from single-channel systems remain, and a new channel selection problem is introduced. In this dissertation, Multi-channel medium access control (MAC) protocols are proposed for mobile ad hoc networks (MANETs) for nodes equipped with a single half-duplex transceiver, using more sophisticated physical layer technologies. These include code division multiple access (CDMA), orthogonal frequency division multiple access (OFDMA), and diversity. CDMA increases channel reuse, while OFDMA enables communication by multiple users in parallel. There is a challenge to using each technology in MANETs, where there is no fixed infrastructure or centralized control. CDMA suffers from the near-far problem, while OFDMA requires channel synchronization to decode the signal. As a result CDMA and OFDMA are not yet widely used. Cooperative (diversity) mechanisms provide vital information to facilitate communication set-up between source-destination node pairs and help overcome limitations of physical layer technologies in MANETs. In this dissertation, the Cooperative CDMA-based Multi-channel MAC (CCM-MAC) protocol uses CDMA to enable concurrent transmissions on each channel. The Power-controlled CDMA-based Multi-channel MAC (PCC-MAC) protocol uses transmission power control at each node and mitigates collisions of control packets on the control channel by using different sizes of the spreading factor to have different processing gains for the control signals. The Cooperative Dual-access Multi-channel MAC (CDM-MAC) protocol combines the use of OFDMA and CDMA and minimizes channel interference by a resolvable balanced incomplete block design (BIBD). In each protocol, cooperating nodes help reduce the incidence of the multi-channel hidden- and exposed-terminal and help address the near-far problem of CDMA by supplying information. Simulation results show that each of the proposed protocols achieve significantly better system performance when compared to IEEE 802.11, other multi-channel protocols, and another protocol CDMA-based.
ContributorsMoon, Yuhan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Huang, Dijiang (Committee member) / Reisslein, Martin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2010