The Effect Of Hot Spots On The Performance Of Mesh--Based Networks

Direct network performance is affected by different design parameters which include number of virtual channels, number of ports, routing algorithm, switching technique, deadlock handling technique, packet size, and buffer size. Another factor that affects network performance is the traffic pattern. In this thesis, we study the effect of hotspot traffic on system performance. Specifically, we study the effect of hotspot factor, hotspot number, and hot spot location on the performance of mesh-based networks. Simulations are run on two network topologies, both the mesh and torus. We pay more attention to meshes because they are widely used in commercial machines. Comparisons between oblivious wormhole switching and chaotic packet switching are reported. Overall packet switching proved to be more efficient in terms of throughput when compared to wormhole switching. In the case of uniform random traffic, it is shown that the differences between chaotic and oblivious routing are indistinguishable. Networks with low number of hotspots show better performance. As the number of hotspots increases network latency tends to increase. It is shown that when the hotspot factor increases, performance of packet switching is better than that of wormhole switching. It is also shown that the location of hotspots affects network performance particularly with the oblivious routers since their achieved latencies proved to be more vulnerable to changes in the hotspot location. It is also shown that the smaller the size of the network the earlier network saturation occurs. Further, it is shown that the chaos router’s adaptivity is useful in this case. Finally, for tori, performance is not greatly affected by hotspot presence. This is mostly due to the symmetric nature of tori

Content-based addressing in hierarchical distributed hash tables

Peer-to-peer networks have drawn their strength from their ability to operate functionally without the use of a central agent. In recent years the development of the structured peer-to-peer network has further increased the distributed nature of p2p systems. These networks take advantage of an underlying distributed data structure, a common one is the distributed hash table (DHT). These peers use this structure to act as equals in a network, sharing the same responsibilities of maintaining and contributing. But herein lays the problem, not all peers are equal in terms of resources and power. And with no central agent to monitor and balance load , the heterogeneous nature of peers can cause many distribution or bottleneck issues on the network and peer levels. This is due to the way in which addresses are allocated in these DHTs. Often this function is carried out by a consistent hashing function. These functions although powerful in their simplicity and effectiveness are the stem of a crucial flaw. This flaw causes the random nature in which addresses are assigned both when considering peer identification and allocating resource ownership. This work proposes a solution to mitigate the random nature of address assignment in DHTs, leveraging two methodologies called hierarchical DHTs and content based addressing. Combining these methods would enable peers to work in cooperative groups of like interested peers in order to dynamically share the load between group members. Group formation and utilization relies on the actual resources a peer willingly shares and is able to contribute rather than a function of the random hash employed by traditional DHT p2p structures

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future