What is Utility Computing and What It Means to You
What is Utility Computing and What It Means to You
In other words, utility computing is the provision of computing resources as a utility, in the same way that the familiar utilities (electricity, water, gas) are provided; on a pay-as-you-use basis. Sometimes utility computing is called “on-demand computing” - the terms are synonymous. In a utility computing model the following resources would be available “on tap”:
∑ CPU time
∑ Cores & clock cycles per second
∑ Floating point processing vs. integer processing (MIPS vs. FLOPS)
∑ RAM (MBytes used)
∑ Access speed / latency time
∑ Data storage (GBytes used)
∑ Maximum I/O throughput (MBytes per second)
∑ Maximum transactions per second (I/O operations per second)
∑ Resilience (eg. RAID level)
∑ Bandwidth (GBytes transferred)
∑ Latency
∑ Network resilience
Before going further, it is worth mentioning grid computing and cloud computing. These terms are often used in the same breath as utility computing, or they are confused with each other. While interconnected, though, they are different concepts (don't worry if the terms below look a bit technical; the important bits are explained further down):
∑ Grid computing is a technical approach spanning an application across multiple computers within one administrative domain (one provider, not necessarily one location).
∑ A compute grid is a collection of computers within one administrative domain capable of hosting a distributed application.
∑ Grid is about infrastructure.
∑ Utility computing is a sales approach, treating computing resources as a utility in the way we treat the familiar utilities (water,gas,electricity etc.). A utility computing provider would sell resources on their own grid(s).
∑ Utility is about business relationships.
∑ Cloud computing means an open market for computing resources; utility computing applied to multiple grids.
∑ A compute cloud is a grid spanning multiple administrative domains with applications able to move between domains in response to cost and SLA requirements.
∑ Cloud is about scale and the computing resource market
Cloud computing is grid computing extended across multiple administrative domains combined with the utility computing sales approach resulting in an open market for computing resource, with the potential for applications to roam between administrative domains (eg. a data centre, a collection of PCs, a super computer), automatically seeking out the most cost effective resources that fit within their SLA requirement. Lets return to utility computing though (which is where you are buying resources from just one provider).
At present utility computing has not become main stream, but it is coming and already the Internet mega-corps are starting to sell resources on their grids, for example Amazon's Elastic Compute Cloud and Google's App Engine. It is not just the big boys moving into that space though, IT hosting companies like Memset are headed in that direction too, and a good way to understand utility computing is to look at the way companies like us are evolving our services.
For example, take one of our Xen-based Miniserver Virtual Machines; a client might initially just want 256MB of RAM and 30GB of disk space, but in time their requirements might grow beyond one machine and onto a cluster of powerful dedicated servers. This approach (allowing the client to start small and grow the resource allocation as needed) gives very large cost savings to them (as well as no up-front capital expenditure) and is very green; we balance the load across our pool of Miniserver host machines to make efficient use of the available disk and CPU resource (bandwidth is secondary since if you don't use it all, it is not really consuming power).
The above is a very crude example of computing as a utility; the next step is our deployment of on-demand clusters where the client has, let’s say 10 servers dedicated to his application, but at normal loads only 3 are required, so only 3 are powered up most of the time. As demand increases our in-house management software spots the trend and (ahead of requirement) brings the other nodes in the cluster online. We plan to incentivise our clients to use this system by billing them separately for electricity, so if they let us turn off the machines that are there just to cope with load spikes and normally not being used, it costs them less.
That is still not true utility computing though since the customer still has hardware dedicated to them. We will be offering true utility computing when we can virtualise customers' server clusters and dynamically allocate them to machines in our server pool that are not necessarily dedicated to that client. That is also when you truly get the big cost and energy savings; imagine us hosting a big online game in the same data centre as a back office function of a large corporate. During the daytime the back office function might need 50 servers to run, and the game only 10, but during the night the game might need 50 and the back office 10.
With traditional provisioning you would have at least 100 machines on and running all the time, but with utility computing you only need 60 or less. In reality it is even worse since no sane CTO would run his application without some overhead room to cope with load spikes, but again you get that for free with utility computing since the load spikes just become a ripple on top of all the baseline operations, saving you even more cost and carbon. I estimate that if all UK data centre operations were running in a true utility computing environment we would be able to reduce our power and hardware requirements by a factor of ten.
Utility computing in the above sense also gives you another big advantage: inherent resilience. If any one server (node) in the hardware pool fails the client's application will carry on running because it will automatically be running on more than one node. This means that even small customers can have the benefit of a server cluster but at a fraction of the normal cost.
There is a catch though; to get the really big savings (in terms of money, energy and hardware) you need to consolidate large numbers of diverse applications (with different load characteristics and different usage patterns) into a small number of big data centers, or at least a small number of big utility computing pools. The problem is that most CIOs are still unsure about the security of virtualisation (for no good reason I might add), let alone allowing their applications to "roam" freely across pools of servers, being allocated CPU & disk resources that might have moments ago been used for one of their competitors.
However, as with most big cost-saving & green initiatives, to get the real benefits of utility computing we need to change the way we think and operate at an organisational level - rolling out some shiny new technologies by itself is not enough. In this case we need to lose our outdated attachment to tin and the idea that "this application runs on those boxes there". Instead we should view CPU time and storage space as facilities to be rented as and when they are needed, in much the same way as we do with bandwidth. After all, the routers feeding your 'net connection might have been being used for something quite different other moments before, but we don't care - why should we with servers?
What is Utility Computing and What It Means to You - To learn more about this author, visit Kate Craig-Wood's Website.
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Utility computing is basically what we as an IT hosting provider do but taken to a shorter timescale. We can rapidly provision virtual severs, dedicated servers or server clusters and rent them to customers on flexible terms (eg. monthly contracts). Utility computing is the extension of that concept but instead of talking about servers it is the provision of chunks of general computing resources (CPU, RAM, disk etc) in real-time response to demand, and on short time scales such as hours, minutes or even seconds, and billing the customer for the resource-time used.
In other words, utility computing is the provision of computing resources as a utility, in the same way that the familiar utilities (electricity, water, gas) are provided; on a pay-as-you-use basis. Sometimes utility computing is called “on-demand computing” - the terms are synonymous. In a utility computing model the following resources would be available “on tap”:
∑ CPU time
∑ Cores & clock cycles per second
∑ Floating point processing vs. integer processing (MIPS vs. FLOPS)
∑ RAM (MBytes used)
∑ Access speed / latency time
∑ Data storage (GBytes used)
∑ Maximum I/O throughput (MBytes per second)
∑ Maximum transactions per second (I/O operations per second)
∑ Resilience (eg. RAID level)
∑ Bandwidth (GBytes transferred)
∑ Latency
∑ Network resilience
Before going further, it is worth mentioning grid computing and cloud computing. These terms are often used in the same breath as utility computing, or they are confused with each other. While interconnected, though, they are different concepts (don't worry if the terms below look a bit technical; the important bits are explained further down):
∑ Grid computing is a technical approach spanning an application across multiple computers within one administrative domain (one provider, not necessarily one location).
∑ A compute grid is a collection of computers within one administrative domain capable of hosting a distributed application.
∑ Grid is about infrastructure.
∑ Utility computing is a sales approach, treating computing resources as a utility in the way we treat the familiar utilities (water,gas,electricity etc.). A utility computing provider would sell resources on their own grid(s).
∑ Utility is about business relationships.
∑ Cloud computing means an open market for computing resources; utility computing applied to multiple grids.
∑ A compute cloud is a grid spanning multiple administrative domains with applications able to move between domains in response to cost and SLA requirements.
∑ Cloud is about scale and the computing resource market
Cloud computing is grid computing extended across multiple administrative domains combined with the utility computing sales approach resulting in an open market for computing resource, with the potential for applications to roam between administrative domains (eg. a data centre, a collection of PCs, a super computer), automatically seeking out the most cost effective resources that fit within their SLA requirement. Lets return to utility computing though (which is where you are buying resources from just one provider).
At present utility computing has not become main stream, but it is coming and already the Internet mega-corps are starting to sell resources on their grids, for example Amazon's Elastic Compute Cloud and Google's App Engine. It is not just the big boys moving into that space though, IT hosting companies like Memset are headed in that direction too, and a good way to understand utility computing is to look at the way companies like us are evolving our services.
For example, take one of our Xen-based Miniserver Virtual Machines; a client might initially just want 256MB of RAM and 30GB of disk space, but in time their requirements might grow beyond one machine and onto a cluster of powerful dedicated servers. This approach (allowing the client to start small and grow the resource allocation as needed) gives very large cost savings to them (as well as no up-front capital expenditure) and is very green; we balance the load across our pool of Miniserver host machines to make efficient use of the available disk and CPU resource (bandwidth is secondary since if you don't use it all, it is not really consuming power).
The above is a very crude example of computing as a utility; the next step is our deployment of on-demand clusters where the client has, let’s say 10 servers dedicated to his application, but at normal loads only 3 are required, so only 3 are powered up most of the time. As demand increases our in-house management software spots the trend and (ahead of requirement) brings the other nodes in the cluster online. We plan to incentivise our clients to use this system by billing them separately for electricity, so if they let us turn off the machines that are there just to cope with load spikes and normally not being used, it costs them less.
That is still not true utility computing though since the customer still has hardware dedicated to them. We will be offering true utility computing when we can virtualise customers' server clusters and dynamically allocate them to machines in our server pool that are not necessarily dedicated to that client. That is also when you truly get the big cost and energy savings; imagine us hosting a big online game in the same data centre as a back office function of a large corporate. During the daytime the back office function might need 50 servers to run, and the game only 10, but during the night the game might need 50 and the back office 10.
With traditional provisioning you would have at least 100 machines on and running all the time, but with utility computing you only need 60 or less. In reality it is even worse since no sane CTO would run his application without some overhead room to cope with load spikes, but again you get that for free with utility computing since the load spikes just become a ripple on top of all the baseline operations, saving you even more cost and carbon. I estimate that if all UK data centre operations were running in a true utility computing environment we would be able to reduce our power and hardware requirements by a factor of ten.
Utility computing in the above sense also gives you another big advantage: inherent resilience. If any one server (node) in the hardware pool fails the client's application will carry on running because it will automatically be running on more than one node. This means that even small customers can have the benefit of a server cluster but at a fraction of the normal cost.
There is a catch though; to get the really big savings (in terms of money, energy and hardware) you need to consolidate large numbers of diverse applications (with different load characteristics and different usage patterns) into a small number of big data centers, or at least a small number of big utility computing pools. The problem is that most CIOs are still unsure about the security of virtualisation (for no good reason I might add), let alone allowing their applications to "roam" freely across pools of servers, being allocated CPU & disk resources that might have moments ago been used for one of their competitors.
However, as with most big cost-saving & green initiatives, to get the real benefits of utility computing we need to change the way we think and operate at an organisational level - rolling out some shiny new technologies by itself is not enough. In this case we need to lose our outdated attachment to tin and the idea that "this application runs on those boxes there". Instead we should view CPU time and storage space as facilities to be rented as and when they are needed, in much the same way as we do with bandwidth. After all, the routers feeding your 'net connection might have been being used for something quite different other moments before, but we don't care - why should we with servers?
What is Utility Computing and What It Means to You - To learn more about this author, visit Kate Craig-Wood's Website.
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