And now we come to a topic I’ve been chopping at the bit to talk about, grid computing vs cluster computing. I know, I’m nerding out here in talking about such a specific and field-oriented subject like this. Most people never look at cloud computing beyond the general definition, and don’t take the time to look at the different architectures that can be designed with this sort of system.
But it’s true, so today, we’re pitting grid computing vs cluster computing, and seeing which one mostly wins out in practicality when being applied practically in real world scenarios.
First, let’s define these two architectures for those less nerdy people in the room, shall we? Grid computing is basically like a data center we’ve all seen in recent times. This is a super computer array comprised of parallel machines mounted and interchanged in a two or three dimensional grid or array at a single location. VPS hosting systems use this, many cloud computing services are designed this way, and pretty much all standard hosting is handled by a more discrete version of this concept as well.
Cluster computing is a different animal, and nothing at all like a data center. If it’s like anything, it’s like ants. Yes, ants. Cluster computing uses a wide map of distant, WAN-connected computers or computer arrays, and when a task needs to be done, will cluster several nearby machines with related capacity and low interlink latency to create a node of supercomputing. Sometimes, it will create multiple clusters in disparate areas with high trunk lines between, creating a supercomputer whose parts are diametrically opposite on the planet. This is not unlike ants finding a task, homogenizing through chemical exchange, and working in unison to perform a task.
So, which one wins? Let’s measure the pros and cons of each under specific criteria.
1 – Ease of Maintenance
Grid computing is usually centralized in a data center configuration or something similar. When a device fails or the entire metasystem begins to malfunction, it is easy to maintain control of the situation, and manually address any broken components. Heck, you can power cycle the entire center if need be.
Cluster computing uses a kind of virtual and ambiguous net of potential nodes all interconnected in a web. When tasks need done, nodes interlock to create specific “organs” that perform a function within the virtualized system. If the nodes break, it’s harder to coordinate their repair. However, with clusters, new nodes can be discovered and existing ones rewired a bit on the fly to handle extra purposes a former node may have served. It’s almost neurological.
So, it’s as broad as it is long here, both are about even. Grid is controlled, cluster really doesn’t have these issues.
Score so far: Cluster – 0, Grid – 0
2 – Customer Latency
Grid computing is in a centralized location, and as a result, if users are in a location with high latency talking to the grid’s location, then the system is going to be unavoidably slow. Fixed locations are a problem like this. This is a mark against grid computing.
Cluster computing could theoretically create a node cluster practically around the customer, meaning the only latency is initialization and talking with the central node controller to set the cluster up. Latency by location isn’t nearly the issue here, this is a plus for cluster computing.
Score so far: Cluster – 1, Grid – 0
3 – Security
Security is pretty even for grid and cluster, as far as data exchange goes. Encryption and permissions still work the same in cluster computing as they do in grid, just with a bit more of a soft update table. So neither is in any danger here especially.
However, danger of lack of local control is a problem. A grid is centralized and locally controlled, and therefore all the machines are supervised and easier to prevent tampering or theft of. This is a plus for grid computing.
Cluster computing uses remote computers in locations sometimes mysterious, where computers storing data or running processes may be stolen or tampered with. It rarely happens, but it’s a real possibility. This is a mark against cluster.
Score so far: Cluster – 1, Grid – 1
4 – Scalability
Finally we come to the last, and the tie breaker. Scalability is important, and with grid computing, it’s easy to wind up with overkill or severely deprived resources. The rigidity of a data center is pretty extreme, making updating and expanding costly. Overkills is inevitable after big projects expand a design like this. It’s a mark against grid.
Cluster computing is just potentiality nodes in the form of computers ready to work in the network. When a task is given, the amount of resources needed at that time get configured into a cluster of computing power. When they are no longer needed, they just become potentiality once more. This is a mark for cluster.
Final score: Cluster – 2, Grid – 1
And so we see the winner of grid computing vs cluster computing is in fact cluster. Despite its mild security caveats, its dynamism and lack of concrete design makes it harder to break, and a lot more efficient. It’s also easier to accommodate speeds for different users as well.