20 photos you won’t believe they are in Egypt !

The Amazing Egypt 🙂

Aubergine

20.

siwa Siwa – سيوة

19.

dahab Dahab – دهب

18.

gara Al-Gara cave, Al-Farafra – كهف الجارة, الفرافرة

17.

marsa 3lam red sea Marsa Alam, Red sea – مرسى علم, البحر الأحمر

16.

santa Saint Catherine mountain, Sinai – جبل سانت كاترين, سيناء

15.

aswan Aswan – أسوان

14.

sinai Sinai – سيناء

13.

colored canyon, Nuwebai - الوادي الملون, نويبع colored canyon, Nuwebai – الوادي الملون, نويبع

12.

fiord 5aleeg taba Gulf of Fiord, Taba – خليج فيورد, طابا

11.

gabal mousa Mount Sinai – جبل طور سيناء

10.

mousa Mount Sinai – جبل طور سيناء

9.

nuba Nubia – النوبة

8.

ant Saint Catherine – سانت كاترين

7.

qaron lake faium Qarun lake, Fayoum – بحيرة قارون ,الفيوم

6.

saint Saint Catherine mountain – جبل سانت كاترين

5.

saintt Saint Catherine – سانت كاترين

4.

siwaa Siwa – سيوة

3.

white desert White desert, Farafra – الصحراء البيضاء ,الفرافرة

2.

white dessert White desert, Farafra – الصحراء البيضاء, الفرافرة

1.

معبد فيلة Philae temple, Aswan – معبد فيلة, أسوان

View original post

Scale-out vs Scale-up storage

Scale-out storage is a network-attached storage (NAS) architecture in which the total amount of disk space can be expanded as needed, even if some of the new drives reside in other storage arrays. If and when a given array reaches its storage limit, another array can be added to expand the system capacity.Scale-out storage differs conceptually from the older scale-up approach.

In a scale-up storage system, new hardware can be added and configured as the need arises. The main advantage of the scale-out approach is cost containment, along with more efficient use of hardware resources.

Before scale-out storage became popular, enterprises often purchased storage arrays much larger than needed, to ensure that plenty of disk space would be available for future expansion. If that expansion never occurred or turned out less than expected, much of the originally purchased disk space went to waste. With the scale-out architecture, the initial investment can be more modest; if the storage requirement expands beyond expectations, new arrays can be added as needed, without limit.

In theory, scale up storage appeals because the data center can start small and add capacity and performance as needed. Do these theoretical advantages apply to the use cases in which storage is most commonly deployed; databases and virtualization?

The problem is that scale out storage systems are more expensive to build, implement and maintain. There are many use cases for scale out storage; it is most ideal for situations where meeting a high capacity demand takes precedence over a performance demand.

In scale up architectures, all the performance and capacity potential of the storage system are provided in a single controller unit, typically upfront. Current scale out architectures provide performance and capacity as storage nodes (servers with internal capacity) are added to the infrastructure. These architectures have their ideal use case depending on performance and capacity demands. As stated above, the appeal of a scale out storage system is that performance and capacity can be added incrementally as needed.

Should we scale performance and capacity at same time?

Performance and capacity operate on different vectors and are not necessarily linked together.

In a scale up architecture, all the performance is delivered with the unit upfront where capacity is added, as needed, to the system. While performance can’t necessarily be scaled, it is delivered in its entirety up front and essentially is a fixed cost with no surprises.

One side effect of scale out storage is that the nodes typically need to be homogeneous. Each node needs to have a similar processor chip set and must leverage the exact same size SSDs. A scale up system could intermix SSDs of different sizes and even different types as new flash technology becomes available.

Is Scale-up performance really a big issue?

While the scale up lack of performance scaling is often cited by scale out advocates, the reality is that the overwhelming majority of applications can’t push current scale up systems. Additionally, some scale up systems can do a periodic controller unit upgrade. So as processing technology continues to advance, the head can be upgraded to offer more performance to the existing storage shelves. As a result, there actually is some performance scaling capability in scale up systems.

Some scale up vendors have the ability to add a scale out design to their architecture if the need ever becomes relevant. It is hard to imagine that processing technology would fall behind storage I/O performance, but if it were to happen, this is the ideal way to scale; scale up completely first, then start scaling out if performance exceeds the capabilities of the current processors.

Is Scale-out cheap or at least cheaper than Scale-up?

In storage there are two hard costs to be concerned with. The first is the initial purchase cost. In theory, this should favor a scale out storage system since it can start small. But again, current scale out designs need to have an initial cluster created or they need to deliver high availability in each node. Counting on the cluster for HA requires the purchase of potentially more performance and capacity than the customer needs because more nodes are needed initially. Building HA into each node requires added expense per node, probably equivalent to the scale up storage system.

A case could be made that a storage node could be delivered less expensively than a scale-up controller unit. This would require that the first option be chosen, that nodes are delivered with no HA and require a quorum to do that. Again, buying multiple nodes eliminates that advantage and it leads to node sprawl because nodes have to be added to address performance issues, not capacity issues.

At a minimum, the initial cost difference between the scale up and scale out implementation types may be a wash. When implementation time or time to data is factored into that equation then scale up systems have a clear advantage. It simply takes longer to install more pieces and get those pieces working together.

The second cost, incremental cost, is an area where scale out storage should have an advantage. But again the limits of current scale out designs tell a different story. The only way a scale out All-Flash system would have a cost advantage is if the need for expansion is being driven by performance instead of capacity. But as mentioned earlier, the overwhelming majority of flash vendors and customers report that they can’t exceed the performance of a single box. So any scenario that would justify a scale out deployment will probably not happen in most data centers.

Conclusion: 

A theoretical advantage to scale out is how simple it is to expand. “Like adding Lego blocks” is the common analogy. However, current scale out systems don’t actually “snap” together, they are a series of individual servers with clustering software that must be carefully networked together for maximum performance and availability. This combination makes initial implementation more complex and it makes ongoing upgrades something that needs to be carefully planned.

Scale up architectures are actually relatively simple. All the capabilities, at least from a performance perspective, are delivered upfront. There is nothing to “click” in. Capacity can be added incrementally either by inserting drives into the existing shelf or adding shelves to the existing storage controller. While adding shelves also requires planning, the capacity per shelf is high and as long as the scale-up array can do non-disruptive upgrades, no down time should result.

Scale up storage, while having the disadvantage of buying all the performance capabilities up front, has the dual advantage of more incremental capacity expansion and a less complex backend infrastructure. And leveraging data in-place storage controller upgrades can easily eliminate the lack of performance scalability.

source: Storage Switzerland, EMC, Dell, and 1010data

 

Bringing Unix Philosophy to Big Data

The Unix philosophy fundamentally changed the way we think of computing systems: instead of a sealed monolith, the system became a collection of small, easily understood programs that could be quickly connected in novel and ad hoc ways. Today, big data looks much like the operating systems landscape in the pre-Unix 1960s: complicated frameworks surrounding by a priesthood that must manage and protect a fragile system.

Bryan Cantrill in one of the best Big Data talks; describes and demonstrates Manta, a new object store featuring in situ compute that brings the Unix philosophy to big data, allowing tools like grep, awk and sed to be used in map-reduce fashion on arbitrary amounts of data describing both the design challenges in building Manta (a system built largely in node.js)

The Duel: Timo Boll vs. KUKA Robot

When robot maker Kuka announced that it would be pitting its Agilus robot against table tennis star Timo Boll last month, we expected a fair fight. Conditioned professional human athlete against a cold, merciless, bright orange mechanical arm on a small wooden field, both wielding the same armament: a miniature bat. Boll was once ranked world number one, but Kuka claimed its robot was the quickest in the world. The Agilus was named for its lightning-fast movements, and would presumably be able to rapidly spin into position and return Boll’s balls from anywhere on the table.

Those hoping for a titanic struggle between human and robot will need to wait: Kuka posted the promised video today to muted reactions. The match appears rigged. Boll drops shots to the robotic arm as he hurtles carelessly around the arena, and puts return shots in easy reach of his foe. Soon the table tennis pro is down 6-0. But Boll has a Hollywood-style epiphany — perhaps realizing he’s playing against a programmable arm — and strikes back to take the game with a powerful smash that puts the ball over the top of his opponent.

Meanwhile, the camera crew is more focused on providing Michael Bay-esque slow-motion shots of the action, cutting in and out of rallies in progress to preserve the narrative. A making-of video explains how the crew were able to get the shots — by standing next to the table and sliding a giant camera in front of Boll’s face — but steers clear of showing unedited footage of the match in progress. A match like this could’ve been an intriguing window into future questions of sportsmanship and competitive entertainment; as it is, it’s nothing more than a glorified commercial.