אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Physical memory is the most expensive resource in use in today's cloud computing platforms. Cloud providers would like to maximize their clients' satisfaction by renting precious physical memory to those clients who value it the most. But real-world cloud clients are selfish: they will only tell their providers the truth about how much they value memory when it is in their own best interest to do so. Under these conditions, how can providers find an efficient memory allocation that maximizes client satisfaction? This research presents Ginseng, the first market-driven framework for efficient allocation of physical memory to selfish cloud clients. Ginseng incentivizes selfish clients to bid their true value for the memory they need when they need it. Ginseng continuously collects client bids, finds an efficient memory allocation, and re-allocates physical memory to the clients that value it the most. During the research, a new type of application was evolved, to be used efficiently in a system with dynamic memory conditions. A special benchmark was developed among a modification of a widely used caching application called memcached. It was also necessary to find special configuration of the OS allowing high memory percentage usage without the OS interference. A whole environment was developed around Ginseng, for experimenting and testing the it with different workloads, for simulating it under different conditions, and for comparing the results to determine the system efficiency. Ginseng achieves a x6.2-x15.8 improvement in aggregate client satisfaction when compared with state-of-the-art approaches for cloud memory allocation. It achieves 83%-100% of the optimal aggregate client satisfaction.