Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers

As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator

Efficient and adaptive congestion control for heterogeneous delay-tolerant networks

Detecting and dealing with congestion in delay-tolerant networks (DTNs) is an important and challenging problem. Current DTN forwarding algorithms typically direct traffic towards more central nodes in order to maximise delivery ratios and minimise delays, but as traffic demands increase these nodes may become saturated and unusable. We pro- pose CafRep, an adaptive congestion aware protocol that detects and reacts to congested nodes and congested parts of the network by using implicit hybrid contact and resources congestion heuristics. CafRep exploits localised relative utility based approach to offload the traffic from more to less congested parts of the network, and to replicate at adaptively lower rate in different parts of the network with non-uniform congestion levels. We extensively evaluate our work against benchmark and competitive protocols across a range of metrics over three real connectivity and GPS traces such as Sassy [44], San Francisco Cabs [45] and Infocom 2006 [33]. We show that CafRep performs well, independent of network connectivity and mobility patterns, and consistently outperforms the state-of-the-art DTN forwarding algorithms in the face of increasing rates of congestion. CafRep maintains higher availability and success ratios while keeping low delays, packet loss rates and delivery cost. We test CafRep in the presence of two application scenarios, with fixed rate traffic and with real world Facebook application traffic demands, showing that regardless of the type of traffic CafRep aims to deliver, it reduces congestion and improves forwarding performance

Replicating Web Applications On-Demand

Many Web-based commercial services deliver their content using Web applications that generate pages dynamically based on user profiles, request parameters etc. The workload of these applications are often characterized by a large number of unique requests and a significant fraction of data updates. Hosting these applications drives the need for systems that replicates both the application code and its underlying data. We propose the design of such a system that is based on on-demand replication, where data units are replicated only to servers that access them often. This reduces the consistency overhead as updates are sent to a reduced number of servers. The proposed system allows complete replication transparency to the application, thereby allowing developers to build applications unaware of the underlying data replication. We show that the proposed techniques can reduce the client response time by a factor of 5 in comparison to existing techniques for a realworld e-commerce application used in the TPC-W benchmark. Furthermore, we evaluate our strategies for a wide range of workloads and show that on-demand replication performs better than centralized and fully replicated systems by reducing the average latency of read/write data accesses as well as the amount of bandwidth utilized to maintain data consistency. 1

A Novel Malware Target Recognition Architecture for Enhanced Cyberspace Situation Awareness

The rapid transition of critical business processes to computer networks potentially exposes organizations to digital theft or corruption by advanced competitors. One tool used for these tasks is malware, because it circumvents legitimate authentication mechanisms. Malware is an epidemic problem for organizations of all types. This research proposes and evaluates a novel Malware Target Recognition (MaTR) architecture for malware detection and identification of propagation methods and payloads to enhance situation awareness in tactical scenarios using non-instruction-based, static heuristic features. MaTR achieves a 99.92% detection accuracy on known malware with false positive and false negative rates of 8.73e-4 and 8.03e-4 respectively. MaTR outperforms leading static heuristic methods with a statistically significant 1% improvement in detection accuracy and 85% and 94% reductions in false positive and false negative rates respectively. Against a set of publicly unknown malware, MaTR detection accuracy is 98.56%, a 65% performance improvement over the combined effectiveness of three commercial antivirus products

Object Replication Algorithms for World Wide Web

Object replication is a well-known technique to improve the accessibility of the Web sites. It generally offers reduced client latencies and increases a site's availability. However, applying replication techniques is not trivial and a large number of heuristics have been proposed to decide the number of replicas of an object and their placement in a distributed web server system. This paper presents three object placement and replication algorithms. The first two heuristics are centralized in the sense that a central site determines the number of replicas and their placement. Due to the dynamic nature of the Internet traffic and the rapid change in the access pattern of the World-Wide Web, we also propose a distributed algorithm where each site relies on some locally collected information to decide what objects should be replicated at that site. The performance of the proposed algorithms is evaluated through a simulation study. Also, the performance of the proposed algorithms has been compared with that of three other well-known algorithms and the results are presented. The simulation results demonstrate the effectiveness and superiority of the proposed algorithms

Document replication strategies for geographically distributed web search engines

Cataloged from PDF version of article.Large-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index. Compared to a centralized, single-center search engine, this architecture offers lower query response times as the network latencies between the users and data centers are reduced. However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers. As a remedy to this scalability problem, we propose a document replication framework in which documents are selectively replicated on data centers based on regional user interests. Within this framework, we propose three different document replication strategies, each optimizing a different objective: reducing the potential search quality loss, the average query response time, or the total query workload of the search system. For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers. Moreover, we investigate the performance impact of query forwarding and result caching. We evaluate our strategies via detailed simulations, using a large query log and a document collection obtained from the Yahoo! web search engine. (C) 2012 Elsevier Ltd. All rights reserved

Optimizing the Replication of Multi-Quality Web Applications Using ACO and WoLF

This thesis presents the adaptation of Ant Colony Optimization to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied and replica update loads are minimized. The algorithm proposed, AntDA, for solving this problem is novel in several respects: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Experiments show that AntDA outperforms several other solution methods, but there was room for improvement in the convergence rates of the ants. Therefore, in an attempt to achieve the goals of faster convergence and better solution values for larger problems, AntDA was combined with the variable-step policy hill-climbing algorithm called Win or Learn Fast (WoLF). In experimentation, the addition of this learning algorithm in AntDA provided for faster convergence while outperforming other solution methods

Malware Target Recognition via Static Heuristics

Organizations increasingly rely on the confidentiality, integrity and availability of their information and communications technologies to conduct effective business operations while maintaining their competitive edge. Exploitation of these networks via the introduction of undetected malware ultimately degrades their competitive edge, while taking advantage of limited network visibility and the high cost of analyzing massive numbers of programs. This article introduces the novel Malware Target Recognition (MaTR) system which combines the decision tree machine learning algorithm with static heuristic features for malware detection. By focusing on contextually important static heuristic features, this research demonstrates superior detection results. Experimental results on large sample datasets demonstrate near ideal malware detection performance (99.9+% accuracy) with low false positive (8.73e-4) and false negative rates (8.03e-4) at the same point on the performance curve. Test results against a set of publicly unknown malware, including potential advanced competitor tools, show MaTR’s superior detection rate (99%) versus the union of detections from three commercial antivirus products (60%). The resulting model is a fine granularity sensor with potential to dramatically augment cyberspace situation awareness